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Adaptive Intervention Developed Using SMART

Introductory Example: Using Medication to Prevent Alcoholism Relapse

The SMART in our example is the Extending Treatment Effectiveness of Naltrexone (ExTENd) trial of alcohol-dependence treatments led by David Oslin. In this study, researchers studied the effectiveness of Naltrexone (NTX), an opioid receptor antagonist. An opioid receptor antagonist is a drug that bonds to the opioid receptors in the brain without provoking a response from the receptors. NTX has been used to prevent relapse to alcoholism, but because it diminishes the pleasurable effects of drinking, participants often fail to adhere to NTX regimens.

The researchers sought to answer the following questions:

What level of drinking should be used to define "nonresponse" to NTX?

What treatment should be used when participants do not respond to NTX?

What treatment should be used to prevent relapse among individuals who responded to NTX?

The SMART design tested eight different adaptive interventions that were candidates for how to best use NTX.

What is a SMART design?

Adaptive Intervention:
An intervention that adapts the type or dosage of a treatment based on patient characteristics or response

Adaptive interventions adapt the type or dosage of the intervention based on patient characteristics. Then, the treatment is adjusted repeatedly over time. Because this is a multi-stage process, an adaptive intervention can use a series of decision rules about when and how to modify the intervention. These interventions use individual differences between participants to achieve the best possible outcome, whether that means augmenting an intervention for a non-responsive participant or diminishing treatment for a responsive participant in order to reduce cost or participant burden.

A SMART provides high-quality data for the construction of adaptive interventions. In a SMART, the decision rules for an adaptive intervention are tested. Each participant is randomized into different treatment options when they reach decision points throughout the intervention. A SMART is not an adaptive intervention, but rather a trial that contains multiple adaptive interventions. In a SMART, treatments for participants are randomized at each stage, and the data from the study is then used to design an adaptive intervention in which participants are not randomized: their treatments change based on the intervention's decision rules.

The example below provides details about SMART designs.

About The Study

Example: SMART design for alcohol-dependence intervention

First, all participants were randomized. Both conditions received NTX and MM, but one group was classified as responsive/ nonresponsive using the stringent definition, and the other using the lenient definition.

As soon as a participant met the criteria for non-response, s/he was re-randomized. After eight weeks, if a participant had not met the criteria for non-response, s/he was classified as responsive.

Participants who were non-responsive to NTX+MM were assigned to CBI + MM and were randomized to receive either NTX or a placebo. Participants who responded to initial NTX+MM were assigned either to NTX with no additional support, or to NTX + Phone support. In total, the study lasted 6 months.

First, all participants were randomized. Both conditions received NTX and MM, but one group was classified as responsive/ nonresponsive using the stringent definition, and the other using the lenient definition.

As soon as a participant met the criteria for non-response, s/he was re-randomized. After eight weeks, if a participant had not met the criteria for non-response, s/he was classified as responsive.

Participants who were non-responsive to NTX+MM were assigned to CBI + MM and were randomized to receive either NTX or a placebo. Participants who responded to initial NTX+MM were assigned either to NTX with no additional support, or to NTX + Phone support. In total, the study lasted 6 months.

Data

SMART design:
a trial that provides data for the construction of adaptive interventions

This study had a usable sample size of 250. Multiple imputation was used in the analyses of data for participants who dropped out during the second phase of treatment.

70% white

86% male

28% over age 55

Analysis and results

Lei et al (2012) performed an illustrative analysis on the data. The authors reveal how SMART can provide valuable information about the tailoring variable and indicate which treatments were most efficient and effective.